Development and validation of Artificial Intelligence Based Regional Flood Estimation Model for Eastern Australia

Abstract

This paper presents the development and validation of an artificial intelligence based regional flood frequency analysis (RFFA) model for application in ungauged catchments of eastern Australia. The artificial intelligence based techniques with a flexible model structure and non-linear approach can overcome the limitations of the conventional RFFA models, which are generally based on linear relationship between flood statistics and catchment characteristics. Till now, there have been limited applications of artificial intelligence based techniques to RFFA problems in Australia. This study has developed four artificial intelligence based RFFA models for eastern Australia, using a comprehensive flood database available as a part of Australian Rainfall and Runoff (ARR) revision 'Project 5 Regional flood methods'. These four RFFA models are based on artificial neural network (ANN), genetic algorithm based artificial neural network (GAANN), gene-expression programing (GEP) and co-active neuron fuzzy inference system (CANFIS). A total of 452 catchments from the states of New South Wales, Victoria, Queensland and Tasmania have been considered by this study. These dataset is divided into training and validation sets. Data of 362 catchments (training data set) have been used to train the model and the data from the remaining 90 catchments (validation data set) used to validate the model. The models have been trained/calibrated using the training data set that involved minimisation of the mean squared error between the observed and predicted flood quantiles by the model (being trained) for a given ARI for the training data set. Six average recurrence intervals (ARI) flood quantiles (2, 5, 10, 20, 50 and 100 years) were considered in this study. Four evaluation statistics are adopted to assess the model accuracy: median ratio of the predicted flood quantile (Qpred) and observed flood quantile (Qobs), denoted by Qpred/Qobs ratio, plots of Qobs and Qpred, median relative error and coefficient of efficiency. This is initially done for the training data set and then repeated for the validation data set. The artificial intelligence based RFFA models have been ranked based on their relative performances in relation to the above criteria to identify the best trained/calibrated model. It has been found that none of the four models is superior across all the six ARIs against the adopted criteria. The ANN based RFFA model has a better ranking score in terms of training/calibration; therefore, it is suggested that the ANN-based RFFA model is the best calibrated model among the four artificial intelligence based RFFA models for eastern Australia.